W e study the ambulance relocation problem in which one tries to respond to possible future incidents quickly. For this purpose, we consider compliance table policies: a relocation strategy commonly used in practice. Each compliance table level indicates the desired waiting site locations for the available ambulances. To compute efficient compliance tables, we introduce the minimum expected penalty relocation problem (MEX-PREP), which we formulate as an integer linear program. In this problem, one has the ability to control the number of waiting site relocations. Moreover, different performance measures related to response times, such as survival probabilities, can be incorporated. We show by simulation that the MEXPREP compliance tables outperform both the static policy and compliance tables obtained by the maximal expected coverage relocation problem (MECRP), which both serve as benchmarks. Besides, we perform a study on different relocation thresholds and on two different methods to assign available ambulances to desired waiting sites.
a b s t r a c tIn this paper, we consider an Emergency Medical Services (EMS) system with two types of medical response units: Rapid Responder Ambulances (RRAs) and Regular Transport Ambulances (RTAs). The key difference between both is that RRAs are faster, but they lack the ability to transport a patient to the hospital. To maintain the ability to respond to emergency requests timely when ambulances get busy, we consider compliance tables, which indicate the desired locations of the available ambulances. Our system brings forth additional complexity to the problem of computing optimal compliance tables, as we have two kinds of ambulances. We propose an Integer Linear Program (ILP) computing compliance tables for such a system, which uses outcomes of a Hypercube model as input parameters. Moreover, we include nestedness constraints and we set bounds on the relocation times in the ILP. To obtain more credible results, we simulate the computed compliance tables for different input parameters. Results show that bounding the time a relocation may last is beneficial in certain settings. Besides, including the nestedness constraints ensures that the number of relocations and the relocation time is reduced, while the performance stays unaffected.
Providers of Emergency Medical Services (EMS) are typically concerned with keeping response times short. A powerful means to ensure this, is to dynamically redistribute the ambulances over the region, depending on the current state of the system. In this paper, we provide new insight in how to optimally (re)distribute ambulances. We study the impact of (1) the frequency of redeployment decision moments, (2) the inclusion of busy ambulances in the state description of the system, and (3) the performance criterion on the quality of the distribution strategy. In addition, we consider the influence of the EMS crew workload, such as (4) chain relocations and (5) time bounds, on the execution of an ambulance relocation. To this end, we use trace-driven simulations based on a real-life dataset of ambulance providers in the Netherlands. In doing so, we differentiate between rural and urban regions, which typically face different challenges when it comes to EMS. Our results show that: (1) taking the classical 0-1 performance criterion for assessing the fraction late arrivals only differs slightly from taking expert-opinion based S-curve for evaluating *
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